The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

The reference spatial database for 2019 contains 5142 plots. We use it to calculate a land use map from satellite images. It is organized according to a nested 3-level nomenclature. This is an update of the 2018 database. The sources and techniques used to build the database by land use groups are described below: For agricultural areas, we use a land use database based on farmers' declarations (for EU subsidies). This is the "Registre Parcellaire Graphique" (RPG) published in France by the French Institute for Geographical and Forestry Informations (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. These vector data localize the crops. The release times imply that we use the RPG for last year (2018). It is therefore necessary to verify the good coherence of the data with the image at very high spatial resolution (VHSR) Pleiades. The RPG provides little information on arboriculture. For these classes we called on colleagues specialized in mango, lychee and citrus crops who are familiar with their area and can locate plots in the VHSR image. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product. A random selection of 20% of the polygons in the layer height field allows to keep a diversity of greenhouse types. Each polygon was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was removed. The distinction between mowed and grazed grasslands was completed through collaboration with colleagues from the SELMET joint research unit (Emmanuel Tillard, Expédit Rivière, Colas Gabriel Tovmassian and Jeanne Averna). For natural areas , there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of government departments that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation): a class of shadows due to the island's steep relief (areas not visible because of the cast shade) and a class of vegetation located on steep slopes facing the morning sun called "rampart moor". The polygons for the distinction of savannahs have been improved thanks to the knowledge of Xavier Amelot (CNRS), Béatrice Moppert and Quentin Rivière (University of La Réunion). For wet land areas , the "marsh" and "water" classes were obtained by photo-interpretation of the 2019 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN BD Topo product. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's BD TOPO product. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on 2019 Pleiades image. La base de données spatiale de référence pour 2019, est constituée de 5142 polygones. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'images satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Il s'agit d'une mise à jour de la base de données pour 2018. Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’informations sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Les parcelles de la classe « culture sous serre ou ombrage » sont issues de la couche « bâti industriel » de la BD Topo de l’IGN. Une sélection aléatoire de 20% des polygones dans le champ hauteur de la couche de l’IGN permet de conserver une diversité des types de serre. Chacun des polygones...

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The _location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well _location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well _location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY _location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

These spatial layers were created to provide estimates of non-native species invasion risk across the contiguous United States based on proximity to human population centers and transportation corridors, and proximity to known locations of non-native species. To calculate the human transport risk layer we estimated the proximity to human population centers, transportation corridors, and speed of movement across the landscape. To calculate invasion risk based on known locations of non-native species, we gathered over 30 million records of non-native species occurrences across the contiguous United States from online databases to create nationwide maps of non-native species richness by species type: amphibians, fish, invertebrates, mammals, mollusks, plants, and reptiles.

A current, accurate spatial representation of all historic properties listed on the National Register of Historic Places is of interest to Federal agencies, the National Park Service, State Historic and Tribal Historic Preservation Offices, local government and certified local governments, consultants, academia, and the interested public. This interest stems from the regulatory processes of managing cultural resources that are consistent with the National Historic Preservation Act as Amended (NHPA), the National Environmental Policy Act as Amended, the Archaeological Resources Protection Act, and other laws related to cultural resources. The regulations promulgating these laws require the use of spatial data in support of various decisions and actions related to cultural resource management.The information contained in the feature attribute tables for this dataset is not descriptive. Rather the tables document how the data was created, where it came from, who created the data, what map parameters were used e.g. source scale, source accuracy, source coordinate system etc. Also included is information on the name of the resource, status of the resource i.e. does it still exist, is it restricted and what if any constraints are associated with the resource. Please note that each historic property listed on the National Register has its own nominating history and therefore location information collected in the nominating process is different from one property to another. Therefore metadata has been created for each listed historic property to inform the potential user of the history or lineage of the spatial information associated with the historic property. Locations associated with restricted National Register of Historic Places properties are not included in this GeoDatabase and must be requested from the National Park Service, National Register Program.The metadata in the feature attribute table are compliant with the National Park Serviceâ s Cultural Resource Spatial Data Transfer Standards. These standards were created to facilitate the exchange of spatial data within a variety of contexts, particularly Sections 106 and 110 of NHPA as well as in the context of disaster recovery events. Often locations of National Register listed properties are needed in these situations. The National Register Geo-spatial dataset is organized as a geo-database with feature class definitions based on the National Registerâ s Resource Type designations i.e. historic buildings, historic districts, historic structures, historic objects, and historic sites. The definitions of these types can be found in National Register Bulletin 16A and in the metadata statements for each feature class.

This reference database in vector format (ESRI shape format) is organized according to a multi-level nomenclature. It is used to train an image classification algorithm in order to produce land cover maps of the Antananarivo metropolitan area (see dataverse sheets describing these maps). In order to simplify the work we chose to update the database produced in 2017 and described in: Dupuy et all, 2020 (https://doi.org/10.1016/j.dib.2020.105952). All polygons were verified by photo-interpretation of the Pleiades image acquired on April 3, 2022. GPS points were collected to update some classes and take into account changes since 2017. Each GPS point was converted into a polygon by digitizing the boundaries of the corresponding parcel on a Pleiades image (pixel size of 0.5 m * 0.5 m). These polygons cover the entire study area in order to have a representative view of the existing crop types and urban structures. The final database contains 3113 polygons. Warning, since December 5, 2022 we publish a new version to limit the effects related to flooding (the wetland class was overestimated on the previous version of land cover map). Cette base de données de référence au format vecteur (ESRI shape format) est organisée selon une nomenclature à plusieurs niveaux. Elle est utilisée pour entrainer un algorithme de classification d’images en vue de produire des cartes d’occupation du sol sur l’agglomération d’Antananarivo (Cf. fiches du dataverse décrivant ces cartes). Afin de simplifier les travaux nous avons choisi de mettre à jour la base de données produites en 2017 et décrite dans : Dupuy et all, 2020 (https://doi.org/10.1016/j.dib.2020.105952). Tous les polygones ont été vérifiés par photo-interprétation de l’image Pléiades acquise le 3 avril 2022. Des points GPS ont été collectés pour mettre à jour certaines classes et prendre en compte les évolutions intervenues depuis 2017. Chaque point GPS a été converti en polygone en numérisant les limites de la parcelle correspondante sur une l’image Pléiades (taille de pixel de 0,5 m * 0,5 m). Ces polygones couvrent l’ensemble de la zone d’étude afin d'avoir une représentativité des types de cultures et des structures urbaines existantes. La base de données finale compte 3113 polygones. Attention, depuis le 5 décembre 2022 nous mettons en ligne une nouvelle version pour limiter les effets liés aux inondations (la classe marais était surestimée sur la carte d'occupation du sol).

The reference spatial database for 2017 is composed of 6256 plots. We use it to calculate a land use map from satellite images.It is organized according to a nomenclature offering 3 levels of precision. We randomly selected 20% of the plots in each class to build a validation database while the remaining 80% is used for learning (5002 polygons for learning and 1254 for validation). The following is a brief description of the sources and techniques used to develop it according to land use types : For agricultural areas , we have a land use database based on farmers' declarations to apply for EU subsidies. This is the Registre Parcellaire Graphique (RPG) published in France by the French Institute of Geography (IGN). The description of this data is available here: http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf . These vector data are accurate and can be used as a model to locate crops. The release times imply that we use the RPG for year N -1. It is therefore necessary to check the correct consistency of the data by photo-interpretation of the VHR image. The RPG provides limited information on orchards. For these classes we called on colleagues specialised in mango, lychee and citrus fruit cultivation technicians who are familiar with their sector and can locate plots in the VHR image. Field surveys were conducted using GPS for market gardening crops. The plots of the "greenhouse or shade cultivation" class are derived from the "industrial building" layer of the IGN's "BD Topo" product of IGN. A random selection of 20% of the polygons in the height field of the IGN layer allows to keep a diversity of greenhouse types. Each of the polygons was verified by photo-interpretation of the Pleiades image. If the greenhouse or shade was not visible in the image, the polygon was deleted. For natural areas, there is no regularly updated mapping, but the main classes can be recognized from the GIS layers of the State services that manage these areas (ONF and DEAL). Two specific classes have been added (identified by photo-interpretation) to address the problems of satellite images: a class of shadows due to the island's steep terrain (areas not visible because of the shadow cast) and a class of vegetation located on steep slopes facing the morning sun called "savannah on cliffs". For wet areas, the "marsh", "water" and "hillside retention" classes were obtained by photo-interpretation of the 2017 Pleiades image. These classes are easily recognizable on this type of image. For urban areas we randomly selected polygons from the IGN's BD Topo layer. For the housing type building, 4 building height classes have previously been created (depending on the height of the layer field) in order to preserve a good diversity of the types of buildings present on the island. A random selection of polygons from each class was then made. The "built" layer was completed by a random selection of industrial buildings from the "industrial built" layer of the IGN's TOPO database. This selection was made in the "nature" field of the layer (i‧e. the following types: silo, industrial and livestock). The "photovoltaic panel" class was obtained by photo-interpretation of the polygons on the 2017 Pleiades image. La base de données spatiale de référence terrain pour 2017, est constituée de 6256 parcelles. Nous l'utilisons pour calculer une carte d'occupation du sol à partir d'image satellites. Elle est organisée selon une nomenclature emboitée à 3 niveaux. Nous avons sélectionné de façon aléatoire 20% des parcelles de chaque classe pour constituer une base de donnée de validation alors que les 80% restant sont utilisés pour l’apprentissage (5002 polygones pour l’apprentissage et 1254 pour la validation). Voici une brève description des sources et techniques utilisées pour la constituer en fonction des groupes d’occupation du sol : Pour les espaces agricoles , nous disposons d’une base de données d’occupation du sol basée sur les déclarations que font des agriculteurs pour demander les subventions de l’Union Européenne. Il s’agit du Registre Parcellaire Graphique (RPG) diffusé en France par l’Institut français de l’information géographique et forestière (IGN). La description de cette donnée est disponible ici : http://professionnels.ign.fr/doc/DC_DL_RPG-2-0.pdf. Ces données vecteur sont précises et peuvent servir de modèle pour localiser les cultures. Les délais de diffusion impliquent que nous utilisons le RPG de l’année N -1. Il est donc nécessaire de vérifier la bonne cohérence des données par photo-interprétation de l’image THRS. Le RPG fournit peu d’information sur l’arboriculture. Pour ces classes nous avons fait appel aux collègues techniciens spécialisés dans les cultures de mangues, litchis et agrumes qui connaissent bien leur secteur et peuvent localiser des parcelles sur l’image THRS. Des relevés de terrain ont été réalisés à l’aide d’un GPS pour les cultures de type maraichage. Les parcelles de la classe « culture sous...

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019 and sho

The ORNL DAAC Spatial Data Access Tool (SDAT) is a suite of Web-based applications that enable users to visualize and download spatial data in user-selected spatial/temporal extents, file formats, and projections. SDAT incorporates Open Geospatial Consortium (OGC) standard Web services, including Web Coverage Service (WCS), Web Map Service (WMS), and Web Feature Service (WFS). The SDAT provides ORNL DAAC-archived data sets and additional relevant data products including agriculture, atmosphere, biosphere, climate indicators, human dimensions, land surface, oceans, terrestrial hydrosphere data types, and related model output data sets.

The vector grid system provides a spatial and statistical infrastructure that allows the integration of environmental and socio-economic data. Its exploitation allows the crossing of different spatial data within the same grid units. Project results obtained using this grid system can be more easily linked. This grid system forms the geographic and statistical infrastructure of the Southern Quebec Land Accounts of the Institute of Statistics of Quebec (ISQ). It forms the geospatial and statistical context for the development of ecosystem accounting in Quebec. **In order to improve the vector grid system and the Land Accounts of Southern Quebec and to better anticipate the future needs of users, we would like to be informed of their use (field of application, objectives of use, territory, association with other products, etc.). You can write to us at maxime.keith@stat.gouv.qc.ca **. This grid system allows the spatial integration of various data relating, for example, to human populations, the economy or the characteristics of land. The ISQ wishes to encourage the use of this system in projects that require the integration of several data sources, the analysis of this data at different spatial scales and the monitoring of this data over time. The fixed geographic references of the grids simplify the compilation of statistics according to different territorial divisions and facilitate the monitoring of changes over time. In particular, the grid system promotes the consistency of data at the provincial level. The spatial intersection of the grid and the spatial data layer to be integrated makes it possible to transfer the information underlying the layer within each cell of the grid. In the case of the Southern Quebec Land Accounts, the spatial intersection of the grid and each of the three land cover layers (1990s, 2000s and 2010s) made it possible to report the dominant coverage within each grid cell. The set of matrix files of Southern Quebec Land Accounts is the result of this intersection. **Characteristics: ** The product includes two vector grids: one formed of cells of 1 km² (or 1,000 m on a side), which covers all of Quebec, and another of 2,500 m² cells (or 50 m on a side, or a quarter of a hectare), which fits perfectly into the first and covers Quebec territory located south of the 52nd parallel. Note that the nomenclature of this system, designed according to a Cartesian plan, was developed so that it was possible to integrate cells with finer resolutions (up to 5 meters on a side). In its 2024 update, the 50 m grid system is divided into 331 parts with a side of 50 km in order to limit the number of cells per part of the grid to millions and thus facilitate geospatial processing. This grid includes a total of approximately 350 million cells or 875,000 km2. It is backwards compatible with the 50m grid broadcast by the ISQ in 2018 (spatial structure and unique identifiers are identical, only the fragmentation is different). **Attribute information for 50 m cells: ** * ID_m50: unique code of the cell; * CO_MUN_2022: geographic code of the municipality of January 2022; * CERQ_NV2: code of the natural region of the ecological reference framework of Quebec; * CL_COUV_T50: unique code of the cell; * CL_COUV_T00, CL_COUV_T01: codes for coverage classes Terrestrial maps from the years 1990, 2000 and 2010. Note: the 2000s are covered by two land cover maps: CL_COUV_T01A and CL_COUV_T01b. The first inventories land cover prior to reassessment using the 2010s map, while the second shows land cover after this reassessment process. **Complementary entity classes: ** * Index_grille50m: index of the parts of the grid; * Decoupage_mun_01_2022: division of municipalities; * Decoupage_MRC_01_2022: division of geographical MRCs; * Decoupage_RA_01_2022: division of administrative regions. Source: System on administrative divisions [SDA] of the Ministry of Natural Resources and Forests [MRNF], January 2022, allows statistical compilations to be carried out according to administrative divisions hierarchically superior to municipalities. * Decoupage_CERQ_NV2_2018: division of level 2 of the CERQ, natural regions. Source: Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks [MELCCFP]. Geospatial processes delivered with the grid (only with the FGDB data set) : * ArcGIS ModelBuilder allowing the spatial intersection and the selection of the dominant value of the geographic layer to populate the grid; * ModelBuilder allowing the statistical compilation of results according to various divisions. Additional information on the grid in the report Southern Quebec Land Accounts published in October 2018 (p. 46). View the results of the Southern Quebec Land Accounts on the interactive map of the Institut de la Statistique du Québec.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those …Show full descriptionThe Referrals Spatial Database - Public records locations of referrals submitted to the Department under the Environment Protection and Biodiversity Conservation (EPBC Act) 1999. A proponent (those who are proposing a development) must supply the maximum extent (location) of any proposed activities that need to be assessed under the EPBC Act through an application process. Referral boundaries should not be misinterpreted as development footprints but where referrals have been received by the Department. It should be noted that not all referrals captured within the Referrals Spatial Database, are assessed and approved by the Minister for the Environment, as some are withdrawn before assessment can take place. For more detailed information on a referral a URL is provided to the EPBC Act Public notices pages. Status and detailed planning documentation is available on the EPBC Act Public notices (http://epbcnotices.environment.gov.au/referralslist/). Post September 2019, this dataset is updated using a spatial data capture tool embedded within the Referral form on the department’s website. Users are able to supply spatial data in multiple formats, review spatial data online and submitted with the completed referral form automatically. Nightly processes update this dataset that are then available for internal staff to use (usually within 24 hours). Prior to September 2019, a manual process was employed to update this dataset. In the first instance where a proponent provides GIS data, this is loaded as the polygons for a referral. Where this doesn't exist other means to digitize boundaries are employed to provide a relatively accurate reflection of the maximum extent for which the referral may impact (it is not a development footprint). This sometimes takes the form of heads up digitizing planning documents, sourcing from other state databases (such as PSMA Australia) features and coordinates supplied through the application forms. Any variations to boundaries after the initial referral (i.e. during the assessment, approval or post-approval stages) are processed on an ad hoc basis through a manual update to the dataset. The REFERRALS_PUBLIC_MV layer is a materialized view that joins the spatial polygon data with the business data (e.g. name, case id, type etc.) about a referral. This layer is available for use by the public and is available via a web service and spatial data download. The data for the web service is updated weekly, while the data download is updated quarterly.

This geospatial database gives the land use / land cover type of 2105 georeferenced points, distributed thoughout an area of 84 square kilometers located at the west of Vavatenina town, in the Analanjirofo Region, on the northeastern coast of Madagascar. The first attribute of the shapefile indicates wether the point's class was directly identified in the fields (GT = ground-truth) wether photointerpreted (PI). The second attribute of the shapefile indicates the Land use / land cover class among a list of 9 types: - Built up/road/bare areas, - Annual crops/pasture/short vegetation, - Clove dominated park, - Clove monoculture, - Diversified agroforest, - Diversified park, - Plantation of woody species, - Shrubby fallow, - Woody fallow. These 9 types are the basis of a more complete nomenclature which can be identified by satellite, thus being helpful for remote sensing analyses and mapping of the area. This database can be used for any ecology, agronomy, or social science studies needing spatial information on local land use.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Aerial digital ortho-photography was the foundation imagery for map development. For Abó, the photography was acquired on April May 15, 2002 at a scale of approximately 1:3,000; for Quarai and Gran Quivira it was flown on April 2, 2003 at scales of 1:3,600 and 1:3000, respectively. The 2002-03 digital imagery has a base pixel resolution of 1.0 m. We also made use of statewide 1-meter resolution, true-color imagery from 2005 that became available in 2006 through the New Mexico Resource Geographic Information System. A 10 m spatial resolution USGS Digital Elevation Model (DEM) was used, in conjunction with ground data, to help discriminate between vegetation types based on elevation gradients and terrain. All imagery and other spatial data layers were compiled into a geodatabase and GIS using ArcGIS 9.3 (ESRI 2008).

The Geographic Information Technology (GIT) Services market is experiencing robust growth, driven by increasing adoption of location intelligence across diverse sectors. The market's expansion is fueled by the rising need for precise mapping, spatial data analysis, and improved decision-making capabilities. Factors such as urbanization, infrastructure development, and the burgeoning need for efficient resource management are significantly contributing to the market's upward trajectory. Furthermore, technological advancements, including the proliferation of high-resolution satellite imagery, drone technology, and sophisticated GIS software, are enhancing the capabilities and applications of GIT services. This is leading to wider adoption across various sectors, including urban planning, environmental management, transportation, and agriculture. We estimate the 2025 market size to be approximately $150 billion, based on observed growth trends in related technology sectors and expert analysis. A projected Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033 suggests continued expansion, potentially reaching a value exceeding $275 billion by 2033. While the market presents significant opportunities, certain challenges exist. High initial investment costs for software and hardware, coupled with the need for skilled professionals to operate and interpret the complex data, can pose barriers to entry for smaller companies. Data privacy concerns and the need for robust data security measures also represent challenges that need to be addressed. Despite these restraints, the overall outlook remains positive, driven by ongoing technological innovations and the increasing recognition of the value proposition offered by GIT services across a wide range of industries and applications. The market is segmented by service type (data acquisition, data processing, software development, etc.), industry vertical (government, utilities, etc.), and geographic region. Leading players like Esri, Hexagon, and Pitney Bowes continue to consolidate their market positions, while several regional players also contribute significantly.

The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe’s U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%.